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Text from PDF Page: 002Future Internet 2021, 13, 61 2 of 13 Pandemic Alliance [4,5], which is a cooperative of different organizations sponsored by the foundation that would contribute data for analysis. The Data Collaborative would first be used for COVID-19 efforts and then be reused for other datacentric XPRIZE competitions and challenges. A few existing environments and services have similarities to the Data Collaborative. Google’s Kaggle  is a web-based environment that provides an analytics environment to users at large along with a large collection of publicly datasets. Similar to the Data Collaborative, Kaggle is used for hosting competitions , focusing on machine learning- oriented problems. Google also maintains Colab [8,9], which provides a Python-oriented environment. Available commercial services for hosting analytics environments include CoCalc [10,11] and Nextjournal . The XPRIZE Foundation differs from these operations by not focusing on individual data scientists or consumers and instead focusing on fewer, higher impact technology challenges run by teams of people. We present the design and implementation of the XPRIZE Data Collaborative as a descriptive case study [13,14] of a Cloud Native Application (CNA) [15–18], from re- quirements, design, and implementation to operating experiences. Many case studies and surveys of Cloud implementations focus on particular categories of Cloud Technol- ogy, such as microservices and container technologies [17,19,20] and serverless comput- ing [21–23], but our examination asks the question of whether real CNAs are actually so narrowly focused and if they would use other Cloud technologies, such as Software Defined Infrastructure (SDI)  and even older, seemingly deprecated technologies such as Virtual Machines (VMs). As we describe our implementation choices, we match those choices with common, well-known design Cloud Design patterns [18,19,22,23,25]. Our case study also looks at whether the COVID-19 pandemic affected the Data Collaborative’s design, development, and implementation. In this introduction, we have described critical background information such as the XPRIZE Foundation’s mission and goals for the Data Collaborative. We have listed similar and related services that currently exist and provided context regarding Cloud Native Applications with which we will frame our case study. Section 2 describes the methodology of our efforts, including the goals of our case study, detailed requirements of the Data Collaborative, and the infrastructure design chosen to meet those needs. Section 3 discusses the results of implementing our design, detailing the challenges we encountered and how we handled those challenges. Section 4 talks about insights about Cloud Computing implementations that we have learned from our experiences. Section 5 reviews our findings, describes, possible changes to the Data Collaborative for future competitions, and discusses implications to CNA development from what we learned. 2. Materials and Methods In this section, we describe the key research questions and methodology for our case study on the Data Collaborative. As part of the methodology, we examine the lifecycle of XPRIZE Contests and how these resulted in detailed requirements and individual compo- nents of the eventual solution. A holistic view of the Data Collaborative infrastructure is presented at the end of the section. 2.1. Research Questions and Motivations Our case study of the Data Collaborative Infrastructure attempts to answer the follow- ing questions: 1. Are applications developed in the Cloud moving to only use containers? How are the Cloud technologies ranging from VMs to containers SDIs used and why? 2. How has the COVID-19 pandemic affected application development and deployment? 3. Are there any noticeable trends in Cloud Computing deployment that became appar- ent during the development, and if so, what are they? In addition, we are motivated to add to the literature of case studies for Cloud Computing application deployments. We mentioned in the Introduction that many case
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